Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

Objective: Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs). Methods: EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1 ), second (T2 ), and third (T3 ) visits. The radiographic progression of the (n+1)th visit (Pn+1 =(mSASSSn+1 –mSASSSn )/(Tn+1 – Tn )≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn . We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation. Results: The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

Next-generation sequencing (NGS) is becoming essential in the fields of precision oncology. With implementation of NGS in daily clinic, the needs for continued education, facilitated interpretation of NGS results and optimal treatment delivery based on NGS results have been addressed. Molecular tumor board (MTB) is multidisciplinary approach to keep pace with the growing knowledge of complex molecular alterations in patients with advanced solid cancer. Although guidelines for NGS use and MTB have been developed in western countries, there is limitation for reflection of Korea’s public health environment and daily clinical practice. These recommendations provide a critical guidance from NGS panel testing to final treatment decision based on MTB discussion.